在复杂的多病患者群体中使用数据驱动和临床分拣系统对未满足需求的GP评估:前性队列研究
Emma Parry1,2, Kamran Ahmed3, Simon Evans2
1School of Medicine, Keele University, Staffordshire, UK e.parry@keele.ac.uk.
BJGP open
|June 29, 2023
概括
一个数字医疗保健系统使用风险分层来识别有未满足需求的患者. 积极的病例管理解决了复杂的护理需求,改善了患者的治疗结果,减少了急性服务需求.
科学领域:
- 主要护理是指初级护理.
- 数字卫生系统 数字卫生系统
- 医疗保健管理的管理
背景情况:
- 患有未满足医疗需求的患者往往可以获得非计划的护理.
- 积极识别和管理风险患者可以减少急性服务需求.
- 数据驱动的风险分层是识别需要积极病例管理的患者的关键.
研究的目的:
- 评估一个主动的数字医疗保健系统,以进行全面的需求分析.
- 识别有意外入院和死亡风险的患者.
- 评估在初级保健机构中数字风险分层的有用性.
主要方法:
- 在六个英国的一般实践中进行前性队列研究.
- 数字驱动的风险分层使用七个因素来识别未满足需求的患者.
- 对高风险患者进行进一步的临床评估和未满足需求分析 (UNA).
主要成果:
- 516名患者 (2.1%) 被分组到"关注"组;164名 (0.7%) 接受了UNA.
- 关注的患者更有可能是老年人,女性,PARR得分高,居住在养老院,或在生命末期登记.
- 143名 (87.2%) 接受UNA的患者有计划的检查或转诊;大多数患者有多个领域的需求.
结论:
- 一个集成的,以患者为中心的数字护理系统可以有效地识别不断升级的护理需求.
- 数字系统与全科医生之间的协作对于管理复杂患者至关重要.
- 这种方法有助于解决复杂个体的需求,并优化资源配置.
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